Research Engineer, AI Models

Role Description

We are seeking a Research Engineer to push the boundaries of AI model quality and efficiency. You’ll build fine-tuning pipelines, develop rigorous benchmarking frameworks, and work at the intersection of ML research and hardware-aware optimization—ensuring our models run beautifully on our silicon.

This is a role for someone who thrives at the boundary between research and engineering. You’ll read papers, implement techniques, and ship production-quality code—all in service of making AI inference faster, cheaper, and better.

Key Responsibilities

• Algorithmic Acceleration:
  • Research and implement state-of-the-art techniques to accelerate AI inference—quantization, sparsity, distillation, speculative decoding, caching strategies, and architectural modifications.
  • Systematically characterize tradeoffs between model quality, latency, throughput, and power consumption to find optimal operating points across different use cases.
• Hardware Co-Design:
  • Partner closely with hardware, compiler, and quantization teams to ensure algorithmic improvements translate to real gains on our silicon.
  • Identify optimizations aligned with our architecture's strengths—maximizing throughput while minimizing power.
  • Shape the feedback loop between model development and hardware roadmap.
• Evaluation:
  • Build profiling tools and comprehensive benchmarking frameworks to understand compute bottlenecks, measure model quality across standard and domain-specific evals, and track efficiency metrics.
  • Establish the methodology that informs both algorithmic choices and hardware-software co-design.
• Applied Research:
  • Build robust fine-tuning workflows for modern AI models, enabling rapid experimentation with LoRA, adapters, and full fine-tuning.
  • Stay current with the rapidly evolving landscape—evaluate new architectures, implement promising techniques, and contribute insights that inform technical and go-to-market strategy.

Qualifications

• 5+ years of experience in ML research, applied ML, or ML systems
• Strong fundamentals in Python and PyTorch
• Hands-on experience with modern AI models (transformers, diffusion models, or other generative architectures)
• Experience fine-tuning large models and building training/evaluation pipelines
• Deep understanding of transformers, attention mechanisms, & optimization techniques
• Comfort reading and implementing techniques from research papers

Nice to Have

• Experience with efficient inference techniques (KV cache optimization, attention variants, MoE routing, flow matching)
• Background in hardware-aware ML optimization or quantization
• Familiarity with profiling tools (PyTorch Profiler, Nsight, custom instrumentation)
• Publications in generative modeling, efficient inference, or ML systems
• Contributions to open-source ML projects